code: def get_train_overlap_stub(docs, ngrams_path, ngrams_n_size):
      simulated_overlap = 0.1
      contaminated = int(len(docs) * simulated_overlap)
      return random.sample(range(len(docs)), contaminated)
  
file_path: lm-evaluation-harness/lm_eval/decontamination/decontaminate.py
imports: import time
  import random
  import pickle
  import json
  import glob
  import os
  import collections
  from .janitor import Janitor, word_ngrams
  from .archiver import ZStdTextReader
label_desc: Use image node_id 1 for calling a global function with example usage: get_train_overlap_stub(docs, ngrams_path, ngrams_n_size) and returns: random